What is underfitting?

- [Instructor] In this lesson we're going to talk … about a concept that we briefly mentioned … previously in this course, … and that's the problem of underfitting. … Recall this plot and equation from the last lesson. … Total error is the sum of bias, variance … and some irreducible error that we can't control. … The left side of this plot represents underfitting. … Notice that we're talking about a low complexity model … where we have low variance, but we have high bias, … which results in high total error. … Again, recall that we defined bias … as the algorithm's tendency to consistently learn … the wrong thing by not taking into account … all of the information in the data. … High bias is a result of the algorithm missing … the relevant relationships between features … and target outputs. … And finally, that translates to mean … that underfitting occurs when an algorithm … can't capture the underlying trend of the data. … Now to get a little bit more tangible … this is what underfitting looks like …

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Released

5/10/2019

Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem.